{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdfb9bde-6d0f-4bef-a042-1eccf324cfb2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_Lwf: \u001b[0mweights=./runs/train/increment_VOC_Lwf/weights/last.pt, cfg=models/yolov5s_VisVOCKITTI.yaml, data=data/val_VisDrone_incremental.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=0.001, Lwf_temperature=1.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/dd952ae429e84573b89f0fecf4af2705\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     91704  models.yolo.Detect                      [29, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VisVOCKITTI summary: 217 layers, 7097848 parameters, 7097848 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/increment_VOC_Lwf/weights/last.pt\n",
      "Overriding model.yaml nc=29 with nc=26\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VisVOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/labels\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /root/autodl-tmp/datasets/VisDrone/VisDrone2019-DET-train/images/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 6000 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.95 anchors/target, 0.933 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mWARNING ⚠️ Extremely small objects found: 29644 of 343201 labels are <3 pixels in size\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mRunning kmeans for 9 anchors on 342304 points...\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7493: 100%|████\u001b[0m\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mthr=0.25: 0.9995 best possible recall, 5.74 anchors past thr\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mn=9, img_size=640, metric_all=0.364/0.748-mean/best, past_thr=0.485-mean: 3,5, 4,9, 8,7, 8,15, 16,9, 16,21, 33,17, 29,37, 61,63\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mDone ✅ (optional: update model *.yaml to use these anchors in the future)\n",
      "Plotting labels to runs/train/exp162/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp162\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G     0.1434     0.1196    0.07771       1528        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': Failed to connect to github.com port 443 after 130358 ms: Connection timed out\n",
      "       0/49       3.5G       0.14      0.119    0.07581        431        640: 1\n",
      "tensor([4.52886], device='cuda:0', grad_fn=<AddBackward0>) tensor(2330.20288, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.339     0.0426     0.0175    0.00429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49       6.1G     0.1288     0.1342    0.07067        589        640: 1\n",
      "tensor([4.06633], device='cuda:0', grad_fn=<AddBackward0>) tensor(1544.15234, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.207     0.0761     0.0413     0.0126\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.11G     0.1208     0.1535    0.06775        586        640: 1\n",
      "tensor([4.81488], device='cuda:0', grad_fn=<AddBackward0>) tensor(2207.09131, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.178     0.0896     0.0551     0.0203\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.11G     0.1159     0.1607    0.06568        785        640: 1\n",
      "tensor([4.13002], device='cuda:0', grad_fn=<AddBackward0>) tensor(1692.86353, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707       0.13      0.105      0.066     0.0263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.11G     0.1132      0.166    0.06473        417        640: 1\n",
      "tensor([4.28460], device='cuda:0', grad_fn=<AddBackward0>) tensor(1936.05872, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.126       0.12     0.0745     0.0309\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.11G     0.1115     0.1667    0.06391        276        640: 1\n",
      "tensor([3.45371], device='cuda:0', grad_fn=<AddBackward0>) tensor(1476.90112, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.163      0.132     0.0846      0.036\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.11G     0.1107     0.1678     0.0634        436        640: 1\n",
      "tensor([3.93731], device='cuda:0', grad_fn=<AddBackward0>) tensor(1647.51953, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.199      0.135     0.0908     0.0391\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.11G     0.1101      0.169     0.0628        521        640: 1\n",
      "tensor([3.68929], device='cuda:0', grad_fn=<AddBackward0>) tensor(1284.02124, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.182      0.121      0.085     0.0368\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.183      0.126     0.0888     0.0393\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.11G     0.1087     0.1699    0.06201        498        640: 1\n",
      "tensor([4.38746], device='cuda:0', grad_fn=<AddBackward0>) tensor(2042.38232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.168      0.131     0.0871     0.0387\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.11G     0.1087     0.1695    0.06162        502        640: 1\n",
      "tensor([3.62663], device='cuda:0', grad_fn=<AddBackward0>) tensor(1477.99988, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.151      0.123     0.0838     0.0367\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.11G     0.1078     0.1714    0.06144        568        640: 1\n",
      "tensor([3.96189], device='cuda:0', grad_fn=<AddBackward0>) tensor(1490.89880, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.153      0.129     0.0869     0.0399\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.11G     0.1077     0.1716    0.06099        572        640: 1\n",
      "tensor([4.11437], device='cuda:0', grad_fn=<AddBackward0>) tensor(1585.70813, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.188      0.122     0.0833     0.0373\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.11G     0.1072     0.1699     0.0609        560        640: 1\n",
      "tensor([4.02663], device='cuda:0', grad_fn=<AddBackward0>) tensor(1460.91772, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.147      0.119     0.0779     0.0359\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.11G     0.1071     0.1724     0.0609        466        640: 1\n",
      "tensor([4.36924], device='cuda:0', grad_fn=<AddBackward0>) tensor(2046.41187, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.162      0.133       0.09     0.0416\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.11G     0.1068     0.1712    0.06057        709        640: 1\n",
      "tensor([4.26233], device='cuda:0', grad_fn=<AddBackward0>) tensor(1543.41577, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.145       0.12     0.0822     0.0379\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.11G     0.1069     0.1725    0.06054        440        640: 1\n",
      "tensor([3.72679], device='cuda:0', grad_fn=<AddBackward0>) tensor(1553.87000, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.133      0.124     0.0775      0.036\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.11G     0.1068     0.1727    0.06026        580        640: 1\n",
      "tensor([3.77632], device='cuda:0', grad_fn=<AddBackward0>) tensor(1515.91443, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.126      0.118     0.0754     0.0356\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.11G     0.1062     0.1721    0.06032        503        640: 1\n",
      "tensor([3.54244], device='cuda:0', grad_fn=<AddBackward0>) tensor(1248.86438, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.122      0.113     0.0721      0.034\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.11G     0.1062     0.1732    0.06022        426        640: 1\n",
      "tensor([3.69877], device='cuda:0', grad_fn=<AddBackward0>) tensor(1548.62769, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.109       0.11     0.0687     0.0323\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.11G      0.106     0.1706    0.05982        705        640: 1\n",
      "tensor([3.92927], device='cuda:0', grad_fn=<AddBackward0>) tensor(1616.15356, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.119      0.121     0.0727     0.0343\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.11G     0.1057     0.1706    0.05987        907        640: 1\n",
      "tensor([4.31488], device='cuda:0', grad_fn=<AddBackward0>) tensor(1272.76050, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707       0.14      0.128      0.079     0.0371\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.11G     0.1054     0.1714    0.05963        591        640: 1\n",
      "tensor([3.66080], device='cuda:0', grad_fn=<AddBackward0>) tensor(1137.64453, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.135      0.113     0.0731     0.0349\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.11G     0.1055     0.1697    0.05952        567        640: 1\n",
      "tensor([3.39025], device='cuda:0', grad_fn=<AddBackward0>) tensor(1096.11877, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707       0.11      0.103     0.0656     0.0303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.11G     0.1053     0.1704    0.05944        519        640: 1\n",
      "tensor([3.24556], device='cuda:0', grad_fn=<AddBackward0>) tensor(1023.29468, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.123      0.123     0.0733     0.0342\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.11G      0.105     0.1709    0.05954        751        640: 1\n",
      "tensor([3.56432], device='cuda:0', grad_fn=<AddBackward0>) tensor(1072.52100, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.124      0.116     0.0728     0.0345\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.11G     0.1051      0.171    0.05959        335        640: 1\n",
      "tensor([3.24060], device='cuda:0', grad_fn=<AddBackward0>) tensor(1237.85828, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.122      0.117     0.0723     0.0347\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.11G     0.1048     0.1709    0.05936        754        640: 1\n",
      "tensor([4.04206], device='cuda:0', grad_fn=<AddBackward0>) tensor(1177.62122, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.121      0.111     0.0694     0.0331\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.11G     0.1051     0.1705    0.05931        637        640: 1\n",
      "tensor([3.36536], device='cuda:0', grad_fn=<AddBackward0>) tensor(1064.56946, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.122      0.119     0.0709      0.034\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.11G     0.1054     0.1716    0.05932       1044        640: 1\n",
      "tensor([3.28826], device='cuda:0', grad_fn=<AddBackward0>) tensor(887.24347, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.111      0.114     0.0683     0.0326\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.11G     0.1048     0.1715    0.05934        288        640: 1\n",
      "tensor([2.82532], device='cuda:0', grad_fn=<AddBackward0>) tensor(906.89233, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.117      0.107     0.0673      0.032\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.11G     0.1046      0.171    0.05924        628        640: 1\n",
      "tensor([3.30199], device='cuda:0', grad_fn=<AddBackward0>) tensor(835.07324, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.131      0.119     0.0743     0.0352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.11G     0.1051     0.1719    0.05892        580        640: 1\n",
      "tensor([3.38773], device='cuda:0', grad_fn=<AddBackward0>) tensor(953.06763, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.112      0.108     0.0683     0.0328\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.11G     0.1048     0.1722    0.05898        746        640: 1\n",
      "tensor([3.42206], device='cuda:0', grad_fn=<AddBackward0>) tensor(918.05786, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.111      0.111     0.0674     0.0323\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.11G     0.1044     0.1718    0.05919        455        640: 1\n",
      "tensor([3.06681], device='cuda:0', grad_fn=<AddBackward0>) tensor(796.94556, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.118     0.0995     0.0661     0.0316\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.11G     0.1047     0.1714    0.05908        505        640: 1\n",
      "tensor([3.23102], device='cuda:0', grad_fn=<AddBackward0>) tensor(924.49927, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.112      0.102      0.065     0.0313\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.11G     0.1046     0.1717    0.05892        434        640: 1\n",
      "tensor([3.02828], device='cuda:0', grad_fn=<AddBackward0>) tensor(808.61578, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       6000      17707      0.125      0.101      0.065      0.031\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.11G     0.1049     0.1717    0.05847       1351        640:  "
     ]
    }
   ],
   "source": [
    "# 在有lwf的基础上用lwf增量\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_Lwf.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VisVOCKITTI.yaml \\\n",
    "--data data/val_VisDrone_incremental.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/increment_VOC_Lwf/weights/last.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-3 \\\n",
    "\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be9f191e-f5ee-4166-bbc5-835b3821073e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cb86453-1717-427b-ab2d-003c26c84050",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2848db76-9d62-41a7-b069-cdb700291659",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ea2a83-7297-4dbc-8d54-20b4435ab050",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88a2affc-9742-4375-81aa-d8e8a067484c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pod_layers = ' '.join([str(i) for i in range(24)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25c94c0e-01d2-4a3f-9220-a64cf53af113",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/temp_test.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/5c057f6755a24ea987064ce5c92ed60c\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 2501 ima\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/val.cache... 1048 images, 0 b\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp86/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp86\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      6.05G    0.08426    0.04916    0.07451         32        640: 1\n",
      "tensor([1.18634], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00205, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675     0.0607       0.21     0.0525     0.0252\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.05G    0.06276    0.04577    0.05999         14        640: 1\n",
      "tensor([0.77238], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00242, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675     0.0294      0.469      0.107     0.0522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.05G    0.05693    0.04157     0.0555         33        640: 1\n",
      "tensor([1.41715], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00548, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.112      0.131     0.0626\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.06G    0.05327    0.04014    0.04946         29        640: 1\n",
      "tensor([1.16293], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00500, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.118      0.142     0.0726\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.06G    0.05106    0.03886    0.04531         21        640: 1\n",
      "tensor([1.18699], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00467, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.908      0.129      0.152     0.0736\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.06G    0.04884    0.03893       0.04         21        640: 1\n",
      "tensor([1.09586], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00433, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.138      0.154     0.0798\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.06G    0.04715     0.0381    0.03664         37        640: 1\n",
      "tensor([1.15039], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00446, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.141      0.175     0.0903\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.06G    0.04591    0.03701    0.03356         24        640: 1\n",
      "tensor([1.22331], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00576, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.914      0.131      0.165     0.0869\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.06G    0.04457    0.03627    0.03104         13        640: 1\n",
      "tensor([1.23396], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00549, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.141      0.173     0.0937\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.06G    0.04305    0.03576    0.02941         22        640: 1\n",
      "tensor([0.88241], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00503, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924       0.14      0.182     0.0966\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.06G    0.04246    0.03487    0.02767         32        640: 1\n",
      "tensor([1.03230], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00458, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.147      0.174       0.09\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.06G    0.04216    0.03485    0.02678         24        640: 1\n",
      "tensor([1.05375], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00491, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.911      0.145      0.177     0.0936\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.06G    0.04132    0.03435     0.0257         33        640: 1\n",
      "tensor([0.97072], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00420, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.143      0.172     0.0896\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.06G    0.04048    0.03439    0.02349         15        640: 1\n",
      "tensor([0.86607], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00544, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.138      0.178     0.0954\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.06G    0.03963    0.03388    0.02287         15        640: 1\n",
      "tensor([0.96451], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00476, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.152      0.178      0.092\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.06G    0.03919    0.03365    0.02213         27        640: 1\n",
      "tensor([0.88317], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00440, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.149      0.183      0.097\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.06G    0.03901    0.03348    0.02121         16        640: 1\n",
      "tensor([0.93317], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00473, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.147      0.174      0.091\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.06G    0.03871    0.03316    0.02014         31        640: 1\n",
      "tensor([1.04032], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00549, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.149      0.179     0.0927\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.06G    0.03817    0.03297    0.02009         24        640: 1\n",
      "tensor([0.87071], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00439, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.921      0.148      0.173     0.0923\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.06G    0.03738    0.03251    0.01888         28        640: 1\n",
      "tensor([1.24634], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00517, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.148      0.174     0.0905\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.06G    0.03717    0.03218    0.01855          9        640: 1\n",
      "tensor([1.07121], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00487, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.144      0.173     0.0911\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.06G    0.03685    0.03218    0.01758         37        640: 1\n",
      "tensor([0.93323], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00450, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.149      0.179     0.0946\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.06G    0.03624    0.03169    0.01825         26        640: 1\n",
      "tensor([0.88707], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00412, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.147      0.173     0.0918\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.06G    0.03584    0.03188    0.01759         25        640: 1\n",
      "tensor([0.87747], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00493, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.155      0.176     0.0944\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.06G    0.03528    0.03073    0.01694         30        640: 1\n",
      "tensor([0.90908], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00434, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.154      0.177      0.093\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.06G    0.03477     0.0304    0.01631         19        640: 1\n",
      "tensor([0.85348], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00471, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.155      0.185     0.0987\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.06G    0.03469    0.03039    0.01633         14        640: 1\n",
      "tensor([0.83067], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00497, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.152      0.176     0.0953\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.06G    0.03456    0.03067    0.01533         38        640: 1\n",
      "tensor([0.96352], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00477, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.154      0.176     0.0963\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.06G    0.03353    0.02994    0.01532         20        640: 1\n",
      "tensor([0.68295], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00403, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.929      0.153      0.184     0.0997\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.06G    0.03375    0.03053    0.01474         30        640: 1\n",
      "tensor([1.09635], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00513, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.158      0.179     0.0979\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.06G    0.03372    0.03018    0.01452         21        640: 1\n",
      "tensor([0.85198], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00457, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.155      0.177      0.097\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.06G    0.03274    0.02925    0.01438         29        640: 1\n",
      "tensor([0.90161], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00499, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.154       0.18     0.0998\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.06G    0.03271    0.03028    0.01374         28        640: 1\n",
      "tensor([0.75177], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00421, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.145      0.182     0.0982\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.06G    0.03231    0.02942    0.01404         19        640: 1\n",
      "tensor([0.79786], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00415, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93       0.15      0.184     0.0993\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.06G    0.03211    0.02911    0.01377         22        640: 1\n",
      "tensor([0.68095], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00412, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.152      0.178     0.0969\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.06G    0.03156    0.02946    0.01282         32        640: 1\n",
      "tensor([0.80155], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00420, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.155      0.178     0.0974\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.06G    0.03137    0.02934    0.01262         18        640: 1\n",
      "tensor([0.91630], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00462, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.156      0.185      0.101\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.06G    0.03059    0.02886    0.01254         33        640: 1\n",
      "tensor([0.84170], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00414, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.152      0.186      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.06G    0.03022    0.02891     0.0117         38        640: 1\n",
      "tensor([0.75065], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00357, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.155      0.188      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.06G    0.03013    0.02853    0.01185         46        640: 1\n",
      "tensor([0.78545], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00379, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.156      0.186      0.104\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.06G    0.03018    0.02779    0.01173         27        640: 1\n",
      "tensor([0.69157], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00351, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.156      0.185      0.103\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.06G    0.02938    0.02833    0.01111         18        640: 1\n",
      "tensor([0.75942], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00395, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.152      0.182      0.104\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.06G     0.0296    0.02771    0.01204         24        640: 1\n",
      "tensor([0.78806], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00376, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.153      0.186      0.102\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.06G    0.02916    0.02852    0.01137         21        640: 1\n",
      "tensor([0.68517], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00351, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.157      0.189      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.06G    0.02878    0.02791    0.01086         28        640: 1\n",
      "tensor([0.70438], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00374, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.157      0.183      0.101\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.06G     0.0287    0.02762    0.01101         34        640: 1\n",
      "tensor([0.80134], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.156      0.188      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.06G    0.02862    0.02734    0.01105         29        640: 1\n",
      "tensor([0.71920], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00334, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.154      0.195      0.107\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.06G    0.02799    0.02721     0.0109         23        640: 1\n",
      "tensor([0.65645], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00392, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.156      0.195       0.11\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.06G     0.0274    0.02691    0.01067         28        640: 1\n",
      "tensor([0.68149], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00323, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.152      0.191      0.108\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.06G    0.02744    0.02656    0.01055         19        640: 1\n",
      "tensor([0.61535], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00340, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.155      0.198      0.111\n",
      "\n",
      "50 epochs completed in 0.519 hours.\n",
      "Optimizer stripped from runs/train/exp86/weights/last.pt, 14.4MB\n",
      "Optimizer stripped from runs/train/exp86/weights/best.pt, 14.4MB\n",
      "\n",
      "Validating runs/train/exp86/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.155      0.198      0.111\n",
      "                   car       1048       4012      0.837      0.719      0.804        0.5\n",
      "                   van       1048        431          1          0     0.0733     0.0492\n",
      "                 truck       1048        166          1          0      0.119     0.0683\n",
      "                  tram       1048         56          1          0      0.054     0.0343\n",
      "                person       1048        618      0.554      0.523      0.513      0.226\n",
      "        person_sitting       1048         20          1          0          0          0\n",
      "               cyclist       1048        234          1          0    0.00644    0.00183\n",
      "                  misc       1048        138          1          0     0.0101    0.00788\n",
      "Results saved to \u001b[1mruns/train/exp86\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/5c057f6755a24ea987064ce5c92ed60c\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                         : 0.7732368375982296\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives            : 562.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                     : 0.8042179644253546\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95                 : 0.5002017350800493\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision                  : 0.8368735350097521\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                     : 0.7185942173479561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives             : 2883.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_f1                     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_false_positives        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_mAP@.5                 : 0.006441460351190526\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_mAP@.5:.95             : 0.0018343685109123704\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_precision              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_recall                 : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cyclist_true_positives         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [785]                     : (0.7517672181129456, 3.904674768447876)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]          : (0.052530564117666115, 0.19778051821176956)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]     : (0.025202849540234856, 0.11090358449470833)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]        : (0.029361777822601305, 0.9358587561506098)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]           : (0.11178590038944655, 0.4690501820610822)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_f1                        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_false_positives           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_mAP@.5                    : 0.010134251721779841\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_mAP@.5:.95                : 0.007877753146287428\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_precision                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_recall                    : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     misc_true_positives            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                      : 0.5379856454608483\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives         : 260.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5                  : 0.5130853033507327\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95              : 0.22624324419280084\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision               : 0.5542442705464022\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall                  : 0.5226537216828478\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_f1              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_false_positives : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_mAP@.5          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_mAP@.5:.95      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_precision       : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_recall          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_sitting_true_positives  : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support                 : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives          : 323.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]           : (0.027400746941566467, 0.08425846695899963)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]           : (0.010552127845585346, 0.07451269775629044)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]           : (0.026563486084342003, 0.04915950447320938)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_f1                        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_false_positives           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_mAP@.5                    : 0.053951958659626965\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_mAP@.5:.95                : 0.034256680978125606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_precision                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_recall                    : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tram_true_positives            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_f1                       : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_false_positives          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5                   : 0.11933746470291787\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_mAP@.5:.95               : 0.06825533190957066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_precision                : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_recall                   : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     truck_true_positives           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]             : (0.04115954041481018, 0.0889984741806984)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]             : (0.03285077214241028, 0.07205551862716675)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]             : (0.06547962874174118, 0.11478909850120544)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_f1                         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_false_positives            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5                     : 0.07332471277631053\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_mAP@.5:.95                 : 0.04917779707492711\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_precision                  : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_recall                     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     van_true_positives             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                    : (0.0004960000000000005, 0.07019108280254777)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                    : (0.0004960000000000005, 0.009583609341825903)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                    : (0.0004960000000000005, 0.009583609341825903)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/5c057f6755a24ea987064ce5c92ed60c\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Distillation_layers : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     PODNet_enable       : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     POD_lambda          : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/temp_test.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 50\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp86\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.70 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/temp_test.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5467ce32-4375-4527-8b9d-ad2383c5e5f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50fa992-fec2-43e3-98f7-299f213d96d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "663f7277-8d9f-49dc-9165-ab64fe459b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "pod_layers = '1 3 5 7 13 17 20 23'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0755b7ff-f9d5-466e-96cc-1ff3ea4f4f3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/temp_test.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/e36a1ef5dd924e8eb2df45042a7a3154\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 2501 ima\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/val.cache... 1048 images, 0 b\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp88/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp88\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      4.27G    0.08336    0.04906    0.07396         32        640: 1\n",
      "tensor([1.03118], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00075, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675     0.0507      0.183     0.0508     0.0259\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      4.27G    0.06068    0.04357    0.05856         14        640: 1\n",
      "tensor([0.66358], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00151, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.119      0.111     0.0551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      4.27G    0.05797    0.04129    0.05597         88        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "       2/49      4.27G    0.05517    0.03917    0.05291         33        640: 1\n",
      "tensor([1.00127], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00183, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.117      0.136     0.0585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      4.27G    0.05084    0.03769    0.04551         29        640: 1\n",
      "tensor([0.83082], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00234, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909       0.11      0.145     0.0684\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      4.27G    0.04833    0.03658    0.03971         21        640: 1\n",
      "tensor([0.94499], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00249, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.913      0.126      0.156      0.074\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      4.27G    0.04649    0.03681    0.03406         21        640: 1\n",
      "tensor([0.84160], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00227, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.129      0.155     0.0752\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      4.27G      0.045    0.03601    0.03122         37        640: 1\n",
      "tensor([0.87868], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00220, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.138      0.172     0.0875\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      4.27G    0.04366    0.03495    0.02816         24        640: 1\n",
      "tensor([0.84050], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00255, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.127       0.16     0.0853\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      4.27G    0.04294    0.03434    0.02604         13        640: 1\n",
      "tensor([0.88183], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00259, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.137      0.163     0.0873\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      4.27G    0.04141    0.03397    0.02481         22        640: 1\n",
      "tensor([0.57279], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00236, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.918      0.139       0.17     0.0894\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      4.27G    0.04085     0.0331    0.02306         32        640: 1\n",
      "tensor([0.73958], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00211, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.133      0.167     0.0867\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      4.27G    0.04031    0.03316    0.02203         24        640: 1\n",
      "tensor([0.69067], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.132      0.173     0.0859\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      4.27G    0.03995     0.0325    0.02107         33        640: 1\n",
      "tensor([0.71720], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00201, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.134      0.159     0.0784\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      4.27G    0.03901    0.03246    0.01884         15        640: 1\n",
      "tensor([0.51780], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00244, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.919      0.138      0.174     0.0879\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      4.27G    0.03811     0.0322    0.01865         15        640: 1\n",
      "tensor([0.64009], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00227, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.136      0.167     0.0851\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      4.27G    0.03746    0.03195      0.018         27        640: 1\n",
      "tensor([0.60897], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00210, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.134      0.166     0.0867\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      4.27G     0.0374    0.03173    0.01692         16        640: 1\n",
      "tensor([0.63783], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00220, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.137      0.162     0.0811\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      4.27G    0.03709    0.03165    0.01623         31        640: 1\n",
      "tensor([0.71456], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00252, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.139      0.168     0.0886\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      4.27G     0.0367    0.03117    0.01632         24        640: 1\n",
      "tensor([0.60590], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00210, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.922      0.144       0.17     0.0844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      4.27G    0.03614    0.03057    0.01485         28        640: 1\n",
      "tensor([0.91439], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00231, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.138      0.164     0.0837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      4.27G     0.0357    0.03012    0.01459          9        640: 1\n",
      "tensor([0.74378], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00243, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.927      0.138      0.166     0.0845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      4.27G    0.03514    0.03036    0.01387         37        640: 1\n",
      "tensor([0.67931], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00230, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.138      0.162     0.0824\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      4.27G    0.03444    0.02971    0.01426         26        640: 1\n",
      "tensor([0.63178], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00201, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.136      0.166     0.0846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      4.27G    0.03419    0.02983    0.01369         25        640: 1\n",
      "tensor([0.56743], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00240, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.931      0.145      0.172     0.0903\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      4.27G    0.03388    0.02861    0.01288         30        640: 1\n",
      "tensor([0.63500], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00201, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.926      0.141       0.17     0.0918\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      4.27G    0.03311    0.02831    0.01274         19        640: 1\n",
      "tensor([0.52025], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00220, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.923      0.144       0.17     0.0878\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      4.27G    0.03318    0.02837    0.01229         14        640: 1\n",
      "tensor([0.53839], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.143      0.165     0.0888\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      4.27G    0.03311    0.02871    0.01154         38        640: 1\n",
      "tensor([0.64635], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00223, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.925      0.146      0.164     0.0892\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      4.27G    0.03193    0.02786    0.01174         20        640: 1\n",
      "tensor([0.42456], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00194, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.141      0.166     0.0874\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      4.27G     0.0322    0.02844    0.01105         30        640: 1\n",
      "tensor([0.77012], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00237, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.934      0.135      0.164     0.0875\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      4.27G    0.03184    0.02794    0.01105         21        640: 1\n",
      "tensor([0.56768], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00207, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.924      0.137      0.162     0.0876\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      4.27G    0.03101    0.02689    0.01074         29        640: 1\n",
      "tensor([0.59227], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00240, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.928      0.144      0.167     0.0882\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      4.27G    0.03112    0.02796    0.01029         28        640: 1\n",
      "tensor([0.50699], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00216, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.936      0.139      0.168     0.0896\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      4.27G     0.0304    0.02701    0.01027         19        640: 1\n",
      "tensor([0.55905], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00199, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.93      0.142      0.167     0.0897\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      4.27G    0.03045    0.02684    0.01014         22        640: 1\n",
      "tensor([0.44205], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00208, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.932      0.141      0.165     0.0867\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      4.27G    0.03021    0.02674     0.0095         63        640:  "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/IPython/utils/_process_posix.py:156\u001b[0m, in \u001b[0;36mProcessHandler.system\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;66;03m# res is the index of the pattern that caused the match, so we\u001b[39;00m\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;66;03m# know whether we've finished (if we matched EOF) or not\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m     res_idx \u001b[38;5;241m=\u001b[39m \u001b[43mchild\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpect_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpatterns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_timeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;28mprint\u001b[39m(child\u001b[38;5;241m.\u001b[39mbefore[out_size:]\u001b[38;5;241m.\u001b[39mdecode(enc, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreplace\u001b[39m\u001b[38;5;124m'\u001b[39m), end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/spawnbase.py:383\u001b[0m, in \u001b[0;36mSpawnBase.expect_list\u001b[0;34m(self, pattern_list, timeout, searchwindowsize, async_, **kw)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 383\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mexp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpect_loop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/expect.py:169\u001b[0m, in \u001b[0;36mExpecter.expect_loop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;66;03m# Still have time left, so read more data\u001b[39;00m\n\u001b[0;32m--> 169\u001b[0m incoming \u001b[38;5;241m=\u001b[39m \u001b[43mspawn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_nonblocking\u001b[49m\u001b[43m(\u001b[49m\u001b[43mspawn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspawn\u001b[38;5;241m.\u001b[39mdelayafterread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/pty_spawn.py:500\u001b[0m, in \u001b[0;36mspawn.read_nonblocking\u001b[0;34m(self, size, timeout)\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;66;03m# Because of the select(0) check above, we know that no data\u001b[39;00m\n\u001b[1;32m    498\u001b[0m \u001b[38;5;66;03m# is available right now. But if a non-zero timeout is given\u001b[39;00m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;66;03m# (possibly timeout=None), we call select() with a timeout.\u001b[39;00m\n\u001b[0;32m--> 500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (timeout \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m(spawn, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mread_nonblocking(size)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/pty_spawn.py:450\u001b[0m, in \u001b[0;36mspawn.read_nonblocking.<locals>.select\u001b[0;34m(timeout)\u001b[0m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mselect\u001b[39m(timeout):\n\u001b[0;32m--> 450\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mselect_ignore_interrupts\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchild_fd\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/utils.py:143\u001b[0m, in \u001b[0;36mselect_ignore_interrupts\u001b[0;34m(iwtd, owtd, ewtd, timeout)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mselect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43miwtd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mowtd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mewtd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mInterruptedError\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 16\u001b[0m\n\u001b[1;32m      1\u001b[0m command \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124menv COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124m--img 640 \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msystem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{command}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/ipykernel/zmqshell.py:657\u001b[0m, in \u001b[0;36mZMQInteractiveShell.system_piped\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_ns[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_exit_code\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m system(cmd)\n\u001b[1;32m    656\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_ns[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_exit_code\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43msystem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvar_expand\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/IPython/utils/_process_posix.py:180\u001b[0m, in \u001b[0;36mProcessHandler.system\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    179\u001b[0m         \u001b[38;5;66;03m# Ensure the subprocess really is terminated\u001b[39;00m\n\u001b[0;32m--> 180\u001b[0m         \u001b[43mchild\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mterminate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;66;03m# add isalive check, to ensure exitstatus is set:\u001b[39;00m\n\u001b[1;32m    182\u001b[0m child\u001b[38;5;241m.\u001b[39misalive()\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/pexpect/pty_spawn.py:646\u001b[0m, in \u001b[0;36mspawn.terminate\u001b[0;34m(self, force)\u001b[0m\n\u001b[1;32m    644\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkill(signal\u001b[38;5;241m.\u001b[39mSIGCONT)\n\u001b[0;32m--> 646\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelayafterterminate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    647\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39misalive():\n\u001b[1;32m    648\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/temp_test.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65001b61-b558-407b-94b0-6164e1c2e8db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95248565-3689-4eb1-8aca-9750cd18a25a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd5c2ea0-04aa-467c-b0ba-d80f02b0f719",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acfeca8a-ebc0-4cfe-ba1c-27ff0d748727",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/temp_test.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=1, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/e49c03cc69ac41ec8829c1d0b0b4efc6\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 2501 ima\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/val.cache... 1048 images, 0 b\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp90/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp90\u001b[0m\n",
      "Starting training for 1 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/157 [00:00<?, ?it/s]torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0       3.6G     0.1288    0.04223    0.08857         97        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1271    0.03843    0.08853         63        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1263    0.03787    0.08798         70        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1263    0.03861    0.08803         83        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G      0.126    0.03838    0.08759         74        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1259    0.03923     0.0877        100        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1252     0.0399    0.08771         94        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G      0.125    0.03992    0.08757         84        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1243    0.03944    0.08733         65        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1242    0.03919    0.08755         68        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1241    0.03947    0.08743         95        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G      0.124    0.03939    0.08741         79        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1238    0.03943    0.08722         82        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1238    0.03907    0.08714         69        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1235     0.0396    0.08688        112        640:  torch.Size([16, 3, 80, 80, 31])\n",
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      "        0/0      3.64G     0.1231    0.03949    0.08664         72        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1225    0.03936    0.08646         72        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1223     0.0393    0.08623         81        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G      0.122    0.03929    0.08615         78        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1216    0.03944    0.08607         92        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1215    0.03935    0.08596         83        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1212    0.03931    0.08589         77        640:  torch.Size([16, 3, 80, 80, 31])\n",
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      "        0/0      3.64G     0.1209    0.03957    0.08582        112        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1206    0.03973    0.08571         96        640:  torch.Size([16, 3, 80, 80, 31])\n",
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      "        0/0      3.64G     0.1202    0.03987     0.0857         91        640:  torch.Size([16, 3, 80, 80, 31])\n",
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      "        0/0      3.64G     0.1198    0.03963     0.0856         63        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1194    0.03952    0.08559         80        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G      0.119    0.03924    0.08548         58        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
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      "        0/0      3.64G     0.1186    0.03952     0.0854        109        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1182    0.03959    0.08535         86        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G      0.118    0.03968    0.08538         94        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1175    0.03997    0.08529        104        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1172    0.04008    0.08517         89        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1167    0.04007    0.08506         76        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1162     0.0402    0.08491         88        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1157    0.04037    0.08479         86        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1153    0.04028    0.08462         70        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1149    0.04039    0.08457         92        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1144    0.04047    0.08447         80        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G      0.114    0.04058    0.08444         86        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1136    0.04112    0.08425        122        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1132    0.04097    0.08418         66        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1127    0.04102    0.08403         79        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1122    0.04099    0.08386         75        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1118    0.04105    0.08377         79        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1111    0.04112    0.08343         78        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1106    0.04134    0.08324         92        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1101    0.04138    0.08308         75        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G     0.1097    0.04161    0.08296        102        640:  torch.Size([16, 3, 80, 80, 31])\n",
      "torch.Size([16, 3, 40, 40, 31])\n",
      "torch.Size([16, 3, 20, 20, 31])\n",
      "        0/0      3.64G      0.109    0.04153    0.08285         62        640:  "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/temp_test.yaml \\\n",
    "--epochs 1 \\\n",
    "\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f538fd80-5d66-4f41-af41-eb0aaf7849b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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